Professor Anselin is the developer of the SpaceStat and GeoDa software packages for spatial data analysis. His publications include many hundreds of articles and several edited books in the fields of quantitative geography, regional science, geographic information science, econometrics, economics, and computer science.

Read more about Professor Anselin here.

Bryon Aragam is an Assistant Professor and Topel Faculty Scholar in the Booth School of Business at the University of Chicago. He studies high-dimensional statistics, machine learning, and optimization. His research focuses on mathematical aspects of data science and statistical machine learning in nontraditional settings. Some of his recent projects include problems in graphical modeling, nonparametric statistics, personalization, nonconvex optimization, and high-dimensional inference. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Statistical Software.

Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Bryon has also served as a data science consultant for technology and marketing firms, where he has worked on problems in survey design and methodology, ranking, customer retention, and logistics.

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Since joining Fermilab in 2016, my scientific work has focused on the search of new physics in neutrino oscillation experiments.

As a member of the MicroBooNE Collaboration (since 2016), I am mainly interested in searching for sterile neutrinos, as well as in the development of techniques for the reconstruction and analysis of Liquid Argon Time Project Chambers data. I also joined the DUNE Collaboration, where my main interest is CP violation in the neutrino sector.

Previously I worked for about a decade on the CMS experiments, with major contributions to the Higgs boson discovery, SUSY searches and Standard Model measurements.

I served as CMS Track Reconstruction convener in 2013-2014.

Kyle Chard is a Research Assistant Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

Launa is a software engineer II responsible for executing Data Clinic projects with student teams in conjunction with the 11th Hour Project, as well as internal projects for the DSI. She received her bachelor’s degree in the humanities at Princeton University and her master’s degree in Computational Analysis and Public Policy at the University of Chicago. Prior to joining the University, she worked as an adult education instructor and then as a software consultant at a Microsoft partner company.

Haryadi S. Gunawi is an Associate Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). He received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. He was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012. His current research focuses on cloud computing reliability and new storage technology. He has won numerous awards including NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, NetApp Faculty Fellowships, and Honorable Mention for the 2009 ACM Doctoral Dissertation Award. Website: https://people.cs.uchicago.edu/~haryadi.

My research examines the different ways in which goals, desires and needs affect how people perceive and respond to our environment. My work draws from the traditions of cognitive neuroscience, social psychology and affective science. I use a broad range of methodological tools, including behavioral experiments, computational modeling, fMRI, pupillometry, naturalistic paradigms and network analyses. By combining different tools and perspectives, I seek to characterize motivational influences on human cognition at the psychological, computational and neural levels. One ultimate goal of this work is to identify behavioral and neural targets of intervention to improve socio-cognitive functioning.

I direct the Motivation and Cognition Neuroscience Laboratory at the University of Chicago.

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Ken Nakagaki is an interaction designer and HCI (Human-Computer Interaction) researcher from Japan. He is an assistant professor at the University of Chicago, Computer Science Department. He directs ‘Actuated Experience Lab’ [AxLab] there.

His research has been focuses on inventing novel user interface technologies that seamlessly combine dynamic digital information or computational aids into daily physical tools and materials. He is passionate about creating novel physical embodied experiences using such interfaces through curiosity-driven tangible prototyping processes. During his PhD study at MIT Media Lab, he pursued research in Actuated Tangible User Interfaces, under supervision of Prof. Hiroshi Ishii.

Before joining the Media Lab, he received Master’s and Bachelor’s degrees in interaction design from Keio University. His research has been presented in top HCI conferences (ACM CHI, UIST, TEI). His works were also demonstrated in international exhibitions and museums such as the Ars Electronica Festival and Laval Virtual. He has received numerous awards, including the MIT Technology Review’s Innovators Under 35 Japan & Asia Pacific, the Japan Media Arts Festival, and the James Dyson Award.

Website: https://www.ken-nakagaki.com/about.

Todd is a data scientist for the Data Science Institute. He works on projects associated with the 11th Hour Project and mentors student teams as part of the Data Science Clinic. Todd joined the DSI after completing an MS in Computer Science at the University of Chicago with a focus on data analytics. Todd is interested in both applying data science to scientific and social problems as well as theoretical machine learning. He also owns South Loop Strength & Conditioning, a gym in downtown Chicago, and holds a BA from the University of Illinois Urbana-Champaign in Chemical and Biomolecular Engineering.

Brian Nord uses artificial intelligence to search for clues on the origins and development of the universe. He actively works on statistical modeling of strong gravitational lenses, the cosmic microwave background, and galaxy clusters. As leader of the Deep Skies Lab, he brings together experts in computer science and technology to study questions of cosmology, including dark energy, dark matter, and the early universe, through large-scale data analysis.

Nord has authored or co-authored nearly 50 papers. He trains scientists in public communication, advocates for science education and funding, and works to develop equitable and just research environments. As co-leader of education and public engagement at the Kavli Institute for Cosmological Physics at UChicago, he organizes Space Explorers, a program to help underrepresented minorities in high school engage in hands-on physics experiences outside the classroom. He is an associate scientist at Fermi National Accelerator Laboratory, where he is a member of the Machine Intelligence Group.

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I am an Assistant Professor in the Department of Political Science at The University of Chicago.

I work on quantitative methodology for social science research, with a focus on causal inference, machine learning, and experimental design–particularly for adaptive experiments. My PhD is from Yale, joint in Political Science and Statistics & Data Science.

Previously, I was a post-doctoral fellow in Susan Athey’s Golub Capital Social Impact Lab at the Stanford Graduate School of Business.

In addition to the PhD, I hold a Masters in Statistics, also from Yale, and a Masters in Public Affairs, from the Princeton School of Public and International Affairs. My undergraduate degree was in cultural anthropology from Grinnell College; after college, I spent a year in Lesotho, teaching high school students, and two years in Madagascar, as a Peace Corps volunteer.

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I am an Assistant Professor of Computer Science at the University of Chicago. I received my PhD in Computer Science from Yale University in 2020. My current research explores social dynamics in human-robot interactions, where a robot’s social behaviors lead to positive outcomes for people (e.g., improved team dynamics and performance in a human-robot team, educational learning outcomes for children). During my PhD, I focused on developing robots that improve the performance of human-robot teams by shaping team dynamics to promote inclusion, trust, and cohesion.

Trevor is a Software Engineer II at the DSI. He helps social impact organizations to enhance their operations, research, and communication by utilizing software engineering and data science tools. His work focuses on agriculture, human rights, energy, and marine technology. Trevor also mentors student teams in the Data Science Clinic. Before DSI, Trevor worked as a research assistant at Argonne National Laboratory. Trevor has a BS in Computer Science from MIT.

Sandra Tilmon, MPH, MS is a Healthcare Data Scientist at Data for the Common Good at the University of Chicago. Sandra worked as an Epidemiologist beginning in 2008, and then graduated from the UChicago Data Science program in 2022. Her work has focused on health disparities and safety net health care. LinkedIn

Anna is a Research Data Scientist at the University of Chicago’s Data Science Institute. Her recent work develops methodologies in machine learning for applied problems in medicine, with a particular emphasis on using unsupervised learning of representations to understand image content and structure. She is applying these methods to problems related to understanding and predicting cancer risk, designing personalized cancer screening policies, and designing clinical trials to evaluate AI systems. Previously, Anna served as a postdoctoral fellow at the Data Science Institute, where she was affiliated with the groups of Michael Maire in Computer Science and Olufunmilayo Olopade in the Department of Medicine. She earned her PhD in Physics from the University of Notre Dame, where Kevin Lannon advised her.

Haifeng Xu is an assistant professor in the Department of Computer Science and the Data Science Institute at UChicago. He directs the Strategic IntelliGence for Machine Agents (SIGMA) research lab which focuses on designing algorithms/systems that can effectively elicit, process and exploit information, particularly in strategic environments. Haifeng has published more than 55 publications at leading venues on computational economics, machine learning and theoretical computer science, such as EC, ICML, NeurIPS, STOC and SODA. His research has been recognized by multiple awards, including the Google Faculty Research Award, ACM SIGecom Dissertation Award (honorable mention), IFAAMAS Victor Lesser Distinguished Dissertation Award (runner-up), Google PhD fellowship, and multiple best paper awards.

The following research themes are the recent focus of our research lab. Please refer to our lab’s website for more details.

• The economics of data/information, including selling, acquiring, and exploiting information
• Machine learning in multi-agent setups under information asymmetry, incentive conflicts, and deception
• Resource allocation in adversarial domains, with applications to security and privacy protection

Dr Jai Yu’s research focuses on understanding the neurophysiological mechanisms that support the formation of knowledge. His lab records and analyzes neural data. Prior to joining the University of Chicago, he worked as a data scientist at a Silicon Valley neuromodulation startup where he used advanced data analytics to guide the development of devices for treating neurological conditions.